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9 October 2018 Random forest classification using Sentinel-1 and Sentinel-2 series for vegetation monitoring in the Pays de Brest (France)
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Abstract
Nowadays, optical and radar remote sensing data are increasingly used for land-cover/vegetation mapping and monitoring. Their technical capabilities and tools are improving all the time and provide more accurate results. By the recent arrival of the Sentinel-1 and Sentinel-2 series, available free, processing and methods of analysis must be increased more and more in the field of cartography. This paper aims to present vegetation mapping method in the Pays de Brest area by using a time series stacking of Sentinel-1, Sentinel-2 and SPOT-6 satellites data using the algorithm Random Forest supervised classification. The types of vegetation mapping in first time are those belonging to the major vegetation types, but especially those that can be observed on the processed images that are the Sentinel-1, Sentinel-2 series and SPOT-6. The types of classes considered for this study are: no vegetation, forest and undergrowth, moors and lawns, summer crops, winter crops, grassland and water. Several time series stacking has been made on that series containing 140 images radar representing different dates (2017) and the best combination method is to use both the two polarizations VV and VH to the calculation of the matrix of confusion. On the other hand, combinations of SAR images with different vegetation indices (NDVI, NDWI, S2rep, IRECI) calculated from the Images Sentinel-2 have been made. The series of times series stacking ends with combinations between SPOT-6 and Sentinel-1. The times series stacking Sentinel-1, Sentinel-2 and SPOT-6 are satisfactory, with an overall accuracy that reaches 93%. Such precision is very good for data that are available free.
Conference Presentation
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Simona Niculescu Sr., Antoine Billey, and Halima Talab-Ou-Ali Jr. "Random forest classification using Sentinel-1 and Sentinel-2 series for vegetation monitoring in the Pays de Brest (France)", Proc. SPIE 10783, Remote Sensing for Agriculture, Ecosystems, and Hydrology XX, 1078305 (9 October 2018); https://doi.org/10.1117/12.2325546
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